{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:10:49Z","timestamp":1776784249362,"version":"3.51.2"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society","award":["AC01202101114"],"award-info":[{"award-number":["AC01202101114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cloud Comput."],"published-print":{"date-parts":[[2023,4,1]]},"DOI":"10.1109\/tcc.2022.3184157","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T19:38:48Z","timestamp":1656099528000},"page":"2084-2095","source":"Crossref","is-referenced-by-count":13,"title":["Federated Clouds for Efficient Multitasking in Distributed Artificial Intelligence Applications"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-4668","authenticated-orcid":false,"given":"Yuejin","family":"Li","sequence":"first","affiliation":[{"name":"Chinese University of Hong Kong, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5503-3932","authenticated-orcid":false,"given":"Kai","family":"Hwang","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7836-9372","authenticated-orcid":false,"given":"Kefan","family":"Shuai","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1534-3658","authenticated-orcid":false,"given":"Zhengdao","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3090-1059","authenticated-orcid":false,"given":"Albert","family":"Zomaya","sequence":"additional","affiliation":[{"name":"University of Sydney, Sydney, NSW, Australia"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2019.2916856"},{"key":"ref35","article-title":"Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks","author":"cruz-roa","year":"2014","journal-title":"Medical Imaging 2014 Digital Pathology"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2019.2911831"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01585-4"},{"key":"ref37","article-title":"Types of breast cancer","year":"2022"},{"key":"ref14","article-title":"Federated learning: Collaborative machine learning without centralized training data","author":"mcmahan","year":"2017"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.186902"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/BF01386390"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2013.6461195"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2019.2910251"},{"key":"ref33","first-page":"2041","article-title":"Connected preserving clustering algorithm for wireless sensor networks","volume":"29","author":"xu","year":"2008","journal-title":"J Chin Comput Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2022.3143153"},{"key":"ref32","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2766330.2766337"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2015.7248345"},{"key":"ref17","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2022","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref39","article-title":"The CIFAR-10 dataset","author":"krizhevsky","year":"2022"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000295"},{"key":"ref38","year":"2022"},{"key":"ref19","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"lin","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803001"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3093234"},{"key":"ref23","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n\u00fd","year":"2016"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3119402"},{"key":"ref20","article-title":"Federated learning with non-IID data","author":"zhao","year":"2018"},{"key":"ref42","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref21","article-title":"Multi-hop federated private data augmentation with sample compression","author":"jeong","year":"2019"},{"key":"ref28","article-title":"Distributed cloud\/edge computing in terrestrial and space networks for AIoT and remote sensing applications","author":"li","year":"2022"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2022.3176803"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2022.3192560"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2016.2535215"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2012.203"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2021.3103935"},{"key":"ref4","author":"hwang","year":"2017","journal-title":"Cloud Computing for Machine Learning and Cognitive Applications A Machine Learning Approach"},{"key":"ref3","author":"hwang","year":"2011","journal-title":"Distributed and Cloud Computing From Parallel Processing to the Internet of Things"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2019.2913419"},{"key":"ref5","first-page":"1337","article-title":"Cloud computing: System instances and current research","volume":"20","author":"kang","year":"2009","journal-title":"J Softw"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102422"}],"container-title":["IEEE Transactions on Cloud Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6245519\/10144930\/09805840.pdf?arnumber=9805840","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T18:45:31Z","timestamp":1687805131000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9805840\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,1]]},"references-count":42,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tcc.2022.3184157","relation":{},"ISSN":["2168-7161","2372-0018"],"issn-type":[{"value":"2168-7161","type":"electronic"},{"value":"2372-0018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,1]]}}}